1. Field
The present invention relates to an image processing device, an image processing method, and an image processing control program detecting a saliency region in an image, and a recording medium.
2. Related Art
In the field of image processing, conventionally, an image processing device detecting (extracting), from the image, an image region expected to be noted by a human in an image or a salient region as an image region to be noted is known. By calculating saliency measure in each of points in the image by using such a salient region detecting technique, a salient map image indicative of the saliency measure in each of the points in the image is generated.
Such salient region detection or the salient region detecting technique is used, for example, to detect a subject in an image.
As algorithms for the salient region detection, a learning base algorithm and a physical model base algorithm exist.
In the learning base algorithm, an image processing device is caused to perform learning for the salient region detection by using a large amount of a learning database and, after that, a salient region is detected on the basis of the learning result. On the other hand, in the physical model base algorithm, a salient region is calculated by using an equation obtained by approximating a recognition model of a human or another object.
In the learning base algorithm, the performance of detecting a salient region by the image processing device depends on the content of the learning database. It is, however, difficult to build a learning database of salient regions.
In the physical model base algorithm, the equation obtained by approximating a human recognition model has to be used. However, a human physical model is complicated and cannot be easily expressed by an equation. Perfect definition of a human physical model has not been realized yet.
For example, Japanese Unexamined Patent Application Publication No. 2010-258914 describes a salient region image generating device which extracts a salient region from an image and realizes a region segmentation between the salient region and the other region without building a learning database of images and defining a recognition model in advance.
Concretely, in the salient region image generating device, from input images constructing a frame of an input image, a salient region prior probability image expressing probability of being a salient region and a feature likelihood expressing likelihood of an image feature amount included in each of the salient region and the region other than the salient region. The salient region image generating device extracts a salient region image expressing a salient region in the input image on the basis of the input image, the salient image prior probability image, and the feature amount likelihood.
The literature describes that, consequently, also in the case where prior information regarding an object region and a background region in an input image is not given, the salient region image generating device can extract a salient region from the input image and perform image region segmentation.
E. Rahtu, J. Kannala, M. Salo, and J. Heikkila, “Segmenting salient objects from images and videos”, in Proceedings of European Conference on Computer Vision, 2010 and Z. Tang, Z. Miao, Y. Wan, and J. Li, “Automatic foreground extraction for images and videos” in Proceedings of IEEE International Conference on Image Processing, 2010 disclose a method of calculating a saliency measure by using the Bayes' theorem.